Search Results for author:
Found 9 papers, 1 papers with code
SGMM: Stochastic Approximation to Generalized Method of Moments
We introduce a new class of algorithms, Stochastic Generalized Method of Moments (SGMM), for estimation and inference on (overidentified) moment restriction models.
Bootstraps for Dynamic Panel Threshold Models
This paper develops valid bootstrap inference methods for the dynamic panel threshold regression.
Fast Inference for Quantile Regression with Tens of Millions of Observations
Big data analytics has opened new avenues in economic research, but the challenge of analyzing datasets with tens of millions of observations is substantial.
What Impulse Response Do Instrumental Variables Identify?
Macro shocks are often composites, yet overlooked in the impulse response analysis.
Minimax Risk in Estimating Kink Threshold and Testing Continuity
We derive a risk lower bound in estimating the threshold parameter without knowing whether the threshold regression model is continuous or not.
Regression Discontinuity Design with Potentially Many Covariates
This paper studies the case of possibly high-dimensional covariates in the regression discontinuity design (RDD) analysis.
Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling
We develop a new method of online inference for a vector of parameters estimated by the Polyak-Ruppert averaging procedure of stochastic gradient descent (SGD) algorithms.
Inference for parameters identified by conditional moment restrictions using a generalized Bierens maximum statistic
Many economic panel and dynamic models, such as rational behavior and Euler equations, imply that the parameters of interest are identified by conditional moment restrictions with high dimensional conditioning instruments.
Robust Inference on Infinite and Growing Dimensional Time Series Regression
We develop a class of tests for time series models such as multiple regression with growing dimension, infinite-order autoregression and nonparametric sieve regression.
